Willingness to Pay for Clean Air: Evidence from Air Purifier Markets in China
Ito & Zhang
APEC 8990
Paper Presentations
September 19, 2024
Motivation
- Air quality is poor in developing countries and has negative health and economic consequences
- However, poor air quality may actually be optimal if WTP is low
- Limited empirical evidence of WTP for clean air
Research Question
What is the willingness to pay for clean air?
Overview
- Context: China 2006-2014
- Developing country with poor air quality
- Methods:
- Spatial RDD based on Huai River heating policy
- Product fixed effects and city fixed effects
- Distance to plant IV to capture variation in transportation cost, which is a supply-side cost shifter
- Findings:
- Households have a high WTP for air quality, but heterogeneity exists by income and information
Demand for Air Purifiers
Random Utility Model
- Consumer i in city c can purchase air purifier j at price p_{jc} to reduce indoor air pollution by x_{jc} = x_c \cdot e_j
- Purifier j’s effectiveness at reducing indoor particulate matter e_j \in [0,1]
- Observe markets for c = 1,...,C cities with i=1,...,I_c consumers
Then the conditional indirect utility of consumer i from purchasing air purifier j at city c is
u_{ijc} = \beta_i x_{jc} + \alpha_i \rho_{jc} + \eta_j + \lambda_c + \xi_{jc} + \epsilon_{ijc}
where x_{jc} represents the improvements in indoor air quality conditional on the purchase of product j, \rho_{jc} represents the price of product j in market c, \eta_j represents product fixed effects that capture utility gains from unobserved and observed product characteristics, \lambda_c represents city fixed effects, \xi_{jc} represents a product-city specific demand shock, and \epsilon_{ijc} represents a mean-zero stochastic term.
- \beta_i: marginal utility for clean air
- \alpha_i: marginal disutility of price
Demand for Air Purifiers
Logit Model
Assuming that \beta_i = \beta and \alpha_i = \alpha for consumer i and that the error term is distributed as a type I extreme value function, then consumer i purchases purifier j if u_{ijc} > u_{ikc} for \forall k \ne j. Then the market share for product j in city c is
s_{jc} = \frac{\exp \left( \beta x_{jc} + \alpha p_{jc} + \eta_j + \lambda_c + \xi_{jc} \right)}{\sum_{k=0}^{J} \exp \left( \beta x_{kc} + \alpha p_{kc} + \eta_k + \lambda_c + \xi_{kc} \right)}
which simplifies to
\begin{align*}
s_{jc} &= \beta x_{jc} + \alpha \rho_{jc} + \eta_j + \lambda_c + \xi_{jc} \\
s_{jc} &= \beta x_{c} H_j + \alpha \rho_{jc} + \eta_j + \lambda_c + \xi_{jc}
\end{align*}
- x_{c}: ambient pollution
- H_j: indicator variable for HEPA purifiers
- Identifying variation: x_{c} H_j has city-by-product variation and \rho_{jc} has city-by-product
Empirical Strategy
RD
First Stage:
x_c = \gamma N_c + \phi_1 d_c + \phi_2 d_c N_c + \nu_l + \epsilon_c
where x_c represents PM10 in city c, N_c is the dummy variable for the north, d_c represents the distance between city c and the Huai River, and e_c is the error term.
- \lambda measures a discontinuous change in x_c at the Huai River border
Reduced form RD:
\ln s_{jc} = \rho N_c H_j + \alpha p_{jc} + \left( \psi_1 d_c + \psi_2 d_c N_c + \nu_l \right) H_j + \eta_j + \lambda_c + \epsilon_{jc}
where s_{jc} and p_{jc} respectively represent the market share and the price of product j in city c, \eta_j represents product fixed effects, and \lambda_c represents city fixed effects.
Empirical Strategy
RD + IV
Estimate the MWTP for clean air (second stage):
\ln s_{jc} = \rho x_c H_j + \alpha p_{jc} + \left( \psi_1 d_c + \psi_2 d_c N_c + \nu_l \right) H_j + \eta_j + \lambda_c + \epsilon_{jc}
using N_c H_j as the instrument for x_c H_j
- - \beta / \alpha represents the MWTP for one unit of PM10 (μg/m3)
- Also uses transportation costs from a product’s manufacturing location to its market as an IV for price to capture a a supply-side cost shifter that does not directly affect demand
- Exclusion restriction: the instruments must be uncorrelated with the error term given the control variables and fixed effects
Results
RD design at the Huai River boundary
Results
Standard Logit
Results
Results
Marginal WTP for clean air and household income
Summary
- MWTP for removing 1 μ g/m3 of PM10 per year is $1.34
- Much higher for higher-income households
- Implied value of a statistical life year (VSLY) per person is $455
- Greater than Kremer et al. 2011 ($24) but less than León and Miguel 2017 ($13,500 Africans; $23,232 Non-Africans)
- Drawbacks:
- No information on other indoor avoidance behavior
- Ignores dynamic decision making
- Lack of analysis on market failures